Abstract : A central problem in the theory of genetic algorithms is the characterization of problems that are difficult for GAs to optimize. Many attempts to characterize such problems focus on the notion of deception, defined in terms of the static average fitness of competing schemas. This note argues this popular approach appears unlikely to yield a predictive theory for genetic algorithms. Instead, the characterization of hard problems must take into account the basic features of genetic algorithms, especially their dynamic, biased sampling strategy. (AN)